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#include "caffe2/opt/shape_info.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor_int8.h"
#include "caffe2/utils/string_utils.h"
#include <cctype>
namespace caffe2 {
namespace {
bool isNumber(const std::string& s) {
bool empty = true;
for (const char c : s) {
if (std::isalpha(c)) {
return false;
}
if (!std::isspace(c)) {
empty = false;
}
}
return !empty;
}
std::string toLower(const std::string& s) {
std::string t;
t.resize(s.size());
for (size_t i = 0; i < t.size(); i++) {
t[i] = std::tolower(s[i]);
}
return t;
}
TensorProto_DataType toTensorProtoDataType(const std::string& in) {
std::string s = toLower(in);
if (s == "uint8") {
return TensorProto_DataType_UINT8;
} else if (s == "int8") {
return TensorProto_DataType_INT8;
} else if (s == "uint16") {
return TensorProto_DataType_UINT16;
} else if (s == "int16") {
return TensorProto_DataType_INT16;
} else if (s == "int32") {
return TensorProto_DataType_INT32;
} else if (s == "int64") {
return TensorProto_DataType_INT64;
} else if (s == "float16" || s == "half") {
return TensorProto_DataType_FLOAT16;
} else if (s == "float") {
return TensorProto_DataType_FLOAT;
} else if (s == "double") {
return TensorProto_DataType_DOUBLE;
} else if (s == "byte") {
return TensorProto_DataType_BYTE;
} else if (s == "string") {
return TensorProto_DataType_STRING;
} else if (s == "bool") {
return TensorProto_DataType_BOOL;
} else if (s == "hash") {
return TensorProto_DataType_ZERO_COLLISION_HASH;
}
// return default data type, float
return TensorProto_DataType_FLOAT;
}
} // namespace
ShapeInfo getShapeInfoFromBlob(const Blob* blob) {
ShapeInfo shape_info;
shape_info.shape = GetTensorShapeOfBlob(blob);
if (!shape_info.shape.unknown_shape()) {
shape_info.setDimType(std::vector<TensorBoundShape::DimType>(
shape_info.shape.dims_size(), TensorBoundShape_DimType_CONSTANT));
}
if (blob->meta().id() == TypeMeta::Id<int8::Int8TensorCPU>()) {
shape_info.is_quantized = true;
LoadInt8TensorInfoOfBlob(
&shape_info.q_info.scale,
&shape_info.q_info.offset,
&shape_info.q_info.axis,
blob);
} else {
#ifndef C10_MOBILE
auto function_ptr =
ExternalTensorFunctionsBaseRegistry()->Create(blob->meta().id());
if (function_ptr != nullptr) {
shape_info.is_quantized = function_ptr->isQuantized();
function_ptr->LoadInfoOfBlob(
blob,
&shape_info.q_info.scale,
&shape_info.q_info.offset,
&shape_info.q_info.axis);
}
#endif
}
return shape_info;
}
void modifyTensorShapeDimSize(
TensorShape* tensor_shape,
int dim_index,
const int64_t old_size,
const int64_t new_size) {
CAFFE_ENFORCE(
old_size > 0, "Old size should be non-zero, old_size: ", old_size);
CAFFE_ENFORCE(
tensor_shape->dims(dim_index) % old_size == 0,
"tensor_shape->dims[",
dim_index,
"] = ",
tensor_shape->dims(dim_index),
" cannot be divided by old_size ",
old_size,
" tensor name: ",
tensor_shape->name());
int64_t modified_size = (tensor_shape->dims(dim_index) * new_size) / old_size;
tensor_shape->set_dims(dim_index, modified_size);
}
void changeTensorBoundShapes(
TensorBoundShape& tensor_shape_and_type,
const int64_t old_batch_size,
const int64_t old_seq_size,
const int64_t new_batch_size,
const int64_t new_seq_size) {
CAFFE_ENFORCE(
tensor_shape_and_type.dim_type().size() ==
tensor_shape_and_type.shape().dims().size());
for (int i = 0; i < tensor_shape_and_type.dim_type().size(); i++) {
TensorBoundShape_DimType dim_type = tensor_shape_and_type.dim_type(i);
// Need to change max_batch_size
if (dim_type == TensorBoundShape_DimType_BATCH ||
dim_type == TensorBoundShape_DimType_BATCH_OF_FEATURE_MAX ||
dim_type == TensorBoundShape_DimType_BATCH_OF_FEATURE_MAX_DEFAULT) {
TensorShape* tensor_shape = tensor_shape_and_type.mutable_shape();
modifyTensorShapeDimSize(tensor_shape, i, old_batch_size, new_batch_size);
}
// Need to change max_seq_size
if (dim_type == TensorBoundShape_DimType_BATCH_OF_FEATURE_MAX_DEFAULT ||
dim_type == TensorBoundShape_DimType_FEATURE_MAX_DEFAULT) {
TensorShape* tensor_shape = tensor_shape_and_type.mutable_shape();
modifyTensorShapeDimSize(tensor_shape, i, old_seq_size, new_seq_size);
}
}
}
ShapeInfoMap extractShapeInfoFromTensorBoundShapes(
TensorBoundShapes tensor_bound_shapes,
int64_t new_max_batch_size,
int64_t new_max_feature_len) {
ShapeInfoMap shape_info_map;
if (new_max_batch_size == -1) {
new_max_batch_size = tensor_bound_shapes.max_batch_size();
}
if (new_max_feature_len == -1) {
new_max_feature_len = tensor_bound_shapes.max_feature_len();
}
for (auto& tensor_bound_shape : *(tensor_bound_shapes.mutable_shapes())) {
std::vector<TensorBoundShape::DimType> dim_types;
dim_types.reserve(tensor_bound_shape.shape().dims_size());
for (auto dim_type : tensor_bound_shape.dim_type()) {
dim_types.emplace_back(TensorBoundShape::DimType(dim_type));
}
changeTensorBoundShapes(
tensor_bound_shape,
tensor_bound_shapes.max_batch_size(),
tensor_bound_shapes.max_feature_len(),
new_max_batch_size,
new_max_feature_len);
shape_info_map[tensor_bound_shape.name()] =
// NOLINTNEXTLINE(performance-move-const-arg)
ShapeInfo(dim_types, std::move(tensor_bound_shape.shape()));
}
return shape_info_map;
}
bool operator==(const ShapeInfo& lhs, const ShapeInfo& rhs) {
return lhs.getDimType() == rhs.getDimType() &&
lhs.shape.SerializeAsString() == rhs.shape.SerializeAsString();
}
ShapeInfo constructShapeInfoWithDefaultDimType(
TensorShape shape,
TensorBoundShape_DimType defaultFirstDimType) {
std::vector<TensorBoundShape_DimType> dimType(
shape.dims_size(), TensorBoundShape_DimType_CONSTANT);
if (dimType.size()) {
dimType[0] = defaultFirstDimType;
}
return ShapeInfo(dimType, shape);
}
void parseShapeInfoMapFromString(
const std::string& input,
ShapeInfoMap& shape_hints) {
auto hints = caffe2::split('#', input);
for (const auto& hint : hints) {
auto kv = caffe2::split(',', hint);
CAFFE_ENFORCE_GE(kv.size(), 2, "Cannot parse shape hint: ", hint);
const auto& name = kv[0];
TensorShape shape;
size_t size = kv.size();
CAFFE_ENFORCE_GT(size, 1);
if (!isNumber(kv[size - 1])) {
// last value is the type
shape.set_data_type(toTensorProtoDataType(kv[size - 1]));
size--;
} else {
if (name.find("int8") != std::string::npos) {
// Kept for backwards compatibility.
// Set type explicitly to overwrite it.
shape.set_data_type(TensorProto_DataType_UINT8);
} else {
shape.set_data_type(TensorProto_DataType_FLOAT);
}
}
bool valid = true;
for (size_t i = 1; i < size; i++) {
auto dim = kv[i];
try {
shape.add_dims(std::stoi(dim));
} catch (const std::exception& e) {
valid = false;
CAFFE_THROW("Cannot parse shape hint: ", hint);
}
}
if (valid) {
shape_hints.emplace(name, constructShapeInfoWithDefaultDimType(shape));
}
}
}
} // namespace caffe2
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